mirror of
https://github.com/opencv/opencv.git
synced 2024-12-11 22:59:16 +08:00
7ed557497d
[GAPI] Support basic inference in OAK backend * Combined commit which enables basic inference and other extra capabilities of OAK backend * Remove unnecessary target options from the cmakelist
123 lines
4.1 KiB
C++
123 lines
4.1 KiB
C++
#include <algorithm>
|
|
#include <iostream>
|
|
#include <sstream>
|
|
|
|
#include <opencv2/imgproc.hpp>
|
|
#include <opencv2/imgcodecs.hpp>
|
|
#include <opencv2/gapi.hpp>
|
|
#include <opencv2/gapi/core.hpp>
|
|
#include <opencv2/gapi/imgproc.hpp>
|
|
#include <opencv2/gapi/infer.hpp>
|
|
#include <opencv2/gapi/infer/parsers.hpp>
|
|
#include <opencv2/gapi/render.hpp>
|
|
#include <opencv2/gapi/cpu/gcpukernel.hpp>
|
|
#include <opencv2/highgui.hpp>
|
|
|
|
#include <opencv2/gapi/oak/oak.hpp>
|
|
#include <opencv2/gapi/oak/infer.hpp>
|
|
|
|
const std::string keys =
|
|
"{ h help | | Print this help message }"
|
|
"{ detector | | Path to compiled .blob face detector model }"
|
|
"{ duration | 100 | Number of frames to pull from camera and run inference on }";
|
|
|
|
namespace custom {
|
|
|
|
G_API_NET(FaceDetector, <cv::GMat(cv::GFrame)>, "sample.custom.face-detector");
|
|
|
|
using GDetections = cv::GArray<cv::Rect>;
|
|
using GSize = cv::GOpaque<cv::Size>;
|
|
using GPrims = cv::GArray<cv::gapi::wip::draw::Prim>;
|
|
|
|
G_API_OP(BBoxes, <GPrims(GDetections)>, "sample.custom.b-boxes") {
|
|
static cv::GArrayDesc outMeta(const cv::GArrayDesc &) {
|
|
return cv::empty_array_desc();
|
|
}
|
|
};
|
|
|
|
GAPI_OCV_KERNEL(OCVBBoxes, BBoxes) {
|
|
// This kernel converts the rectangles into G-API's
|
|
// rendering primitives
|
|
static void run(const std::vector<cv::Rect> &in_face_rcs,
|
|
std::vector<cv::gapi::wip::draw::Prim> &out_prims) {
|
|
out_prims.clear();
|
|
const auto cvt = [](const cv::Rect &rc, const cv::Scalar &clr) {
|
|
return cv::gapi::wip::draw::Rect(rc, clr, 2);
|
|
};
|
|
for (auto &&rc : in_face_rcs) {
|
|
out_prims.emplace_back(cvt(rc, CV_RGB(0,255,0))); // green
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace custom
|
|
|
|
int main(int argc, char *argv[]) {
|
|
cv::CommandLineParser cmd(argc, argv, keys);
|
|
if (cmd.has("help")) {
|
|
cmd.printMessage();
|
|
return 0;
|
|
}
|
|
|
|
const auto det_name = cmd.get<std::string>("detector");
|
|
const auto duration = cmd.get<int>("duration");
|
|
|
|
if (det_name.empty()) {
|
|
std::cerr << "FATAL: path to detection model is not provided for the sample."
|
|
<< "Please specify it with --detector options."
|
|
<< std::endl;
|
|
return 1;
|
|
}
|
|
|
|
// Prepare G-API kernels and networks packages:
|
|
auto detector = cv::gapi::oak::Params<custom::FaceDetector>(det_name);
|
|
auto networks = cv::gapi::networks(detector);
|
|
|
|
auto kernels = cv::gapi::combine(
|
|
cv::gapi::kernels<custom::OCVBBoxes>(),
|
|
cv::gapi::oak::kernels());
|
|
|
|
auto args = cv::compile_args(kernels, networks);
|
|
|
|
// Initialize graph structure
|
|
cv::GFrame in;
|
|
cv::GFrame copy = cv::gapi::oak::copy(in); // NV12 transfered to host + passthrough copy for infer
|
|
cv::GOpaque<cv::Size> sz = cv::gapi::streaming::size(copy);
|
|
|
|
// infer is not affected by the actual copy here
|
|
cv::GMat blob = cv::gapi::infer<custom::FaceDetector>(copy);
|
|
// FIXME: OAK infer detects faces slightly out of frame bounds
|
|
cv::GArray<cv::Rect> rcs = cv::gapi::parseSSD(blob, sz, 0.5f, true, false);
|
|
auto rendered = cv::gapi::wip::draw::renderFrame(copy, custom::BBoxes::on(rcs));
|
|
// on-the-fly conversion NV12->BGR
|
|
cv::GMat out = cv::gapi::streaming::BGR(rendered);
|
|
|
|
auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out, rcs))
|
|
.compileStreaming(std::move(args));
|
|
|
|
// Graph execution
|
|
pipeline.setSource(cv::gapi::wip::make_src<cv::gapi::oak::ColorCamera>());
|
|
pipeline.start();
|
|
|
|
cv::Mat out_mat;
|
|
std::vector<cv::Rect> out_dets;
|
|
int frames = 0;
|
|
while (pipeline.pull(cv::gout(out_mat, out_dets))) {
|
|
std::string name = "oak_infer_frame_" + std::to_string(frames) + ".png";
|
|
|
|
cv::imwrite(name, out_mat);
|
|
|
|
if (!out_dets.empty()) {
|
|
std::cout << "Got " << out_dets.size() << " detections on frame #" << frames << std::endl;
|
|
}
|
|
|
|
++frames;
|
|
if (frames == duration) {
|
|
pipeline.stop();
|
|
break;
|
|
}
|
|
}
|
|
std::cout << "Pipeline finished. Processed " << frames << " frames" << std::endl;
|
|
return 0;
|
|
}
|